10 papers
Learning from Acceptance: Cumulative Regret in the Game of Coding
Hanzaleh Akbari Nodehi, Parsa Moradi, Mohammad Ali Maddah-Ali
Classical coding-theoretic guarantees often rely on trust assumptions, such as requiring sufficiently many honest nodes compared with adversarial ones. These assumptions are diffic…
\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments
Hanzaleh Akbari Nodehi, Parsa Moradi, Soheil Mohajer +1
Decentralized machine learning often relies on outsourcing computations, such as gradient evaluations, to untrusted worker nodes. Existing robust aggregation methods can mitigate m…
DReS: Dual Reconstruction Smoothing for Functional Regularization
Parsa Moradi, Tayyebeh Jahaninezhad, Hanzaleh Akbarinodehi +1
Smoothness is a key inductive bias in machine learning and is closely related to generalization. Existing smoothness-inducing methods typically rely either on explicit gradient reg…
Game of Coding for Vector-Valued Computations
Hanzaleh Akbari Nodehi, Parsa Moradi, Soheil Mohajer +1
Traditional coding theory guarantees valid decoding only if a minority of symbols are adversarially manipulated. In contrast, the game of coding framework ensures reliable decoding…
General Coded Computing in a Probabilistic Straggler Regime
Parsa Moradi, Mohammad Ali Maddah-Ali
Coded computing has demonstrated promising results in addressing straggler resiliency in distributed computing systems. However, most coded computing schemes are designed for exact…
Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework
Parsa Moradi, Behrooz Tahmasebi, Mohammad Ali Maddah-Ali
Coded computing has emerged as a promising framework for tackling significant challenges in large-scale distributed computing, including the presence of slow, faulty, or compromise…